It is worth noticing that the benchmark datasets proposed by [1]} were chosen to test feature selection algorithms mainly with the goal of improving model size and comprehensibility, not its predictive performance. On most datasets, using all the supplied features ensures very good predictions. On the other hand, RFE, FRFE, \(k\) -SRFE, as well as other wrapper methods are mostly used with the purpose of improving model predictions. Therefore, in the following section we apply and compare the algorithms on a real-world dataset with noisy and highly correlated features, which impede classification performance.
